code: def plot_prediction_grid(
      model, images, dim=None, figsize=None, score_filter=0.6
  ):
      """Plots a grid of images with boxes drawn around predicted objects.
  
      :param model: The trained model with which to run object detection.
      :type model: detecto.core.Model
      :param images: An iterable of images to plot. If the images are
          normalized torch.Tensor images, they will automatically be
          reverse-normalized and converted to PIL images for plotting.
      :type images: iterable
      :param dim: (Optional) The dimensions of the grid in the format
          ``(rows, cols)``. If no value is given, the grid is of the shape
          ``(len(images), 1)``. ``rows * cols`` must match the number of
          given images, or a ValueError is raised. Defaults to None.
      :type dim: tuple or None
      :param figsize: (Optional) The size of the entire grid in the format
          ``(width, height)``. Defaults to None.
      :type figsize: tuple or None
      :param score_filter: (Optional) Minimum score required to show a
          prediction. Defaults to 0.6.
      :type score_filter: float
  
      **Example**::
  
          >>> from detecto.core import Model
          >>> from detecto.utils import read_image
          >>> from detecto.visualize import plot_prediction_grid
  
          >>> model = Model.load('model_weights.pth', ['tick', 'gate'])
          >>> images = []
          >>> for i in range(4):
          >>>     image = read_image('image{}.jpg'.format(i))
          >>>     images.append(image)
          >>> plot_prediction_grid(model, images, dim=(2, 2), figsize=(8, 8))
      """
  
      # If not specified, show all in one column
      if dim is None:
          dim = (len(images), 1)
  
      if dim[0] * dim[1] != len(images):
          raise ValueError(
              "Grid dimensions do not match size of list of images"
          )
  
      fig, axes = plt.subplots(dim[0], dim[1], figsize=figsize)
  
      # Loop through each image and position in the grid
      index = 0
      for i in range(dim[0]):
          for j in range(dim[1]):
              image = images[index]
              preds = model.predict(image)
  
              # If already a tensor, reverse normalize it and turn it back
              if isinstance(image, torch.Tensor):
                  image = transforms.ToPILImage()(
                      reverse_normalize(image)
                  )
              index += 1
  
              # Get the correct axis
              if dim[0] <= 1 and dim[1] <= 1:
                  ax = axes
              elif dim[0] <= 1:
                  ax = axes[j]
              elif dim[1] <= 1:
                  ax = axes[i]
              else:
                  ax = axes[i, j]
  
              ax.imshow(image)
  
              # Plot boxes and labels
              for label, box, score in zip(*preds):
                  if score >= score_filter:
                      width, height = box[2] - box[0], box[3] - box[1]
                      initial_pos = (box[0], box[1])
                      rect = patches.Rectangle(
                          initial_pos,
                          width,
                          height,
                          linewidth=1,
                          edgecolor="r",
                          facecolor="none",
                      )
                      ax.add_patch(rect)
  
                      ax.text(
                          box[0] + 5,
                          box[1] - 10,
                          "{}: {}".format(
                              label, round(score.item(), 2)
                          ),
                          color="red",
                      )
                  ax.set_title("Image {}".format(index))
  
      plt.show()
  
file_path: detecto/detecto/visualize.py
imports: import cv2
  import matplotlib.patches
  import matplotlib.pyplot
  import torch
  from detecto.utils import reverse_normalize, normalize_transform, _is_iterable
  from torchvision import transforms
label_desc: Use image node_id 3 for calling a global function with example usage: plot_prediction_grid(model, images, dim, figsize, score_filter) without return types